# Kalshi Weather Markets Data — KXHIGH & KXLOW (`bigdavidson/kalshi-weather-markets`) Actor Live bracket prices and settlement history for every Kalshi KXHIGH/KXLOW weather market: per-bracket bid/ask, open interest, settled temps by city and date. 20 cities mapped to their exact settlement stations. No login, no API keys. From $1 per 1,000 markets. - **URL**: https://apify.com/bigdavidson/kalshi-weather-markets.md - **Developed by:** [Jack Sheward](https://apify.com/bigdavidson) (community) - **Categories:** Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 1,000 market records This Actor is paid per event and usage. You are charged both the fixed price for specific events and for Apify platform usage. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event ## What's an Apify Actor? Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases. In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours, and optionally produces a well-defined JSON output, datasets with results, or files in key-value store. In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server. Actors are written with capital "A". ## How to integrate an Actor? If asked about integration, you help developers integrate Actors into their projects. You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready. The best way to integrate Actors is as follows. In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.md): ```bash pip install apify-client ``` In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md): ````bash # MacOS / Linux curl -fsSL https://apify.com/install-cli.sh | bash # Windows irm https://apify.com/install-cli.ps1 | iex ```bash In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/platform/integrations/mcp.md). If your project is in a different language, use the [REST API](https://docs.apify.com/api/v2.md). For usage examples, see the [API](#api) section below. For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt). # README ## Kalshi Weather Markets Data — KXHIGH & KXLOW Pull **Kalshi weather markets** data — every KXHIGH (daily high temperature) and KXLOW (daily low temperature) bracket market — as clean JSON: live bid/ask, volume, open interest, plus full **settlement history** with the official settled temperature per city per day. Covers all 20 Kalshi temperature cities, each mapped to the **exact weather station the market settles on** (including the gotchas: Chicago = Midway, Dallas = DFW, Houston = Hobby). Uses Kalshi's public market-data API directly — no login, no API key, no scraping. ### What you get One record per market (bracket): | Field | Example | Notes | |---|---|---| | `ticker` | `KXHIGHNY-26JUN10-T82` | Kalshi market ticker | | `event_ticker` | `KXHIGHNY-26JUN10` | groups all brackets of one city-day | | `city` | `NYC` | canonical city name | | `station_id` | `KNYC` | ICAO station the market settles on | | `series_ticker` | `KXHIGHNY` | | | `series_type` | `KXHIGH` | `KXHIGH` or `KXLOW` | | `bracket_label` | `83° or above` | human-readable bracket | | `strike_type` | `greater` | `greater`, `less`, or `between` | | `floor_strike` | `82` | bracket lower bound in °F (`null` for "or below" brackets) | | `cap_strike` | `null` | bracket upper bound in °F (`null` for "or above" brackets) | | `status` | `finalized` | `active` (live) or `finalized` (settled) | | `result` | `no` | `yes` / `no` / `null` while live | | `settlement_temp_f` | `82.0` | official settled temperature (settled markets only) | | `settlement_time` | `2026-06-11T12:01:51Z` | | | `open_time` | `2026-06-09T14:00:00Z` | ISO timestamp | | `close_time` | `2026-06-11T04:59:00Z` | ISO timestamp | | `yes_bid` | `0.01` | USD per contract, 0.00–1.00 | | `yes_ask` | `0.02` | USD per contract, 0.00–1.00 | | `last_price` | `0.02` | USD per contract, 0.00–1.00 | | `volume` | `64488.09` | contracts (fractional trading enabled) | | `open_interest` | `31144.75` | contracts (fractional trading enabled) | | `liquidity` | `0.0` | USD value of resting orders | | `trades` | array | only with `includeTrades` — up to 1,000 recent fills | | `orderbook` | object | only with `includeOrderbook` — bid levels, see below | > **Optional keys:** the `trades` and `orderbook` keys exist in a record **only when the corresponding flag is enabled** — with the flag off the key is omitted entirely (not `null`), so read them with `.get()` rather than direct indexing. With the flag on, a per-market fetch failure leaves the key present with value `null`. > > **Orderbook structure:** `orderbook` is `{"yes_dollars": [[price, size], ...], "no_dollars": [[price, size], ...]}` — despite the names (Kalshi's own field names, passed through), each value is a **list of bid levels**, not a single dollar amount: `price` in USD per contract, `size` in (fractional) contracts. Example settled record: ```json { "ticker": "KXHIGHNY-26JUN10-B81.5", "event_ticker": "KXHIGHNY-26JUN10", "city": "NYC", "station_id": "KNYC", "series_ticker": "KXHIGHNY", "series_type": "KXHIGH", "bracket_label": "81° to 82°", "strike_type": "between", "floor_strike": 81, "cap_strike": 82, "status": "finalized", "result": "yes", "settlement_temp_f": 82.0, "settlement_time": "2026-06-11T12:01:51.253194Z", "open_time": "2026-06-09T14:00:00Z", "close_time": "2026-06-11T04:59:00Z", "yes_bid": 0.0, "yes_ask": 1.0, "last_price": 0.99, "volume": 48549.61, "open_interest": 22770.89, "liquidity": 0.0 } ```` Export as JSON, CSV, or Excel directly from the dataset. ### Input Default (live markets, one city — fast, a handful of records): ```json { "cities": ["NYC"], "seriesType": "both", "mode": "live" } ``` Settled-history example (intentionally heavier — ~1,000–2,400 records for two cities at 90 days): ```json { "cities": ["NYC", "Chicago"], "seriesType": "both", "mode": "settled", "lookbackDays": 90 } ``` - `cities` — city names or aliases (`NYC`, `Philly`, `SF`, `NOLA`, `DFW`…), or `"all"` for every supported city. - `seriesType` — `KXHIGH` (daily highs), `KXLOW` (daily lows), or `both`. - `mode` — `live` (currently tradeable brackets with current prices), `settled` (finalized markets with results and settled temperatures), or `both`. - `lookbackDays` — 1–365, settled-history window (only used in `settled`/`both` mode). - `includeTrades` — attach up to 1,000 recent fills per market (one extra API call per market). - `includeOrderbook` — attach the current order book per market (one extra API call per market). All prices are USD per contract (each contract pays $1.00). Quantities (`volume`, `open_interest`, trade `count_fp`, book sizes) are in contracts; Kalshi allows fractional contracts, so they can be non-integer. ### Supported cities | City | Aliases | KXHIGH series | KXLOW series | Settlement station | |---|---|---|---|---| | NYC | New York, NY | KXHIGHNY | KXLOWTNYC | KNYC (Central Park) | | Chicago | — | KXHIGHCHI | KXLOWTCHI | KMDW (Midway, **not O'Hare**) | | Miami | — | KXHIGHMIA | KXLOWTMIA | KMIA | | Austin | — | KXHIGHAUS | KXLOWTAUS | KAUS | | Los Angeles | LA | KXHIGHLAX | KXLOWTLAX | KLAX | | Denver | — | KXHIGHDEN | KXLOWTDEN | KDEN | | Phoenix | — | KXHIGHTPHX | KXLOWTPHX | KPHX | | Philadelphia | Philly | KXHIGHPHIL | KXLOWTPHIL | KPHL | | Houston | — | KXHIGHTHOU | KXLOWTHOU | KHOU (Hobby, **not Bush/IAH**) | | Minneapolis | Min | KXHIGHTMIN | KXLOWTMIN | KMSP | | Oklahoma City | OKC | KXHIGHTOKC | KXLOWTOKC | KOKC | | San Francisco | SF | KXHIGHTSFO | KXLOWTSFO | KSFO | | Washington DC | DC | KXHIGHTDC | KXLOWTDC | KDCA | | Boston | — | KXHIGHTBOS | KXLOWTBOS | KBOS | | Dallas | DFW | KXHIGHTDAL | KXLOWTDAL | KDFW (**not Love Field**) | | Seattle | — | KXHIGHTSEA | KXLOWTSEA | KSEA | | Las Vegas | LV, Vegas | KXHIGHTLV | KXLOWTLV | KLAS | | Atlanta | — | KXHIGHTATL | KXLOWTATL | KATL | | San Antonio | SATX | KXHIGHTSATX | KXLOWTSATX | KSAT | | New Orleans | NOLA | KXHIGHTNOLA | KXLOWTNOLA | KMSY | ### Pricing Pay per market record — no subscription. A live pull for one city is typically 6–14 records; 90 days of settled history for one city is ~500–1,200 records. ### Why this instead of scraping the Kalshi website? - **Structured market data, not HTML.** Direct from Kalshi's public market-data API — stable JSON, no scrape fragility, no login wall. - **Settlement-grade joins.** Every market is mapped to the exact ICAO station its NWS Climatological Report settles on — the mapping mistakes (O'Hare vs Midway, Love Field vs DFW, Bush vs Hobby) that cost real traders real money are already handled. - **Backtest-ready history.** Settled mode gives you bracket, result, and the official settled temperature per city-day in one flat table — join it against any weather dataset by `station_id` and date. ### FAQ #### Which Kalshi weather markets are covered? All daily high (KXHIGH\*) and daily low (KXLOW\*) temperature bracket markets across Kalshi's 20 cities. Series are discovered live from the API, so newly listed cities in these families appear automatically once mapped. #### What units are the prices in? USD per contract, 0.00–1.00 (each contract settles at $1.00). `yes_bid` 0.04 means 4 cents. Volume and open interest are in contracts and can be fractional, since Kalshi supports fractional trading. #### How do I get settled temperature history for a city? Set `mode` to `settled`, pick your `cities` and `lookbackDays`. Each settled record carries `settlement_temp_f` — the official high (or low) Kalshi settled on — plus the winning/losing bracket via `result`. #### Which weather station does each market settle on? See the table above. Kalshi settles on NWS Climatological Reports for one specific station per city — Chicago is Midway (KMDW), Dallas is DFW airport (KDFW), Houston is Hobby (KHOU). Every record carries `station_id` so you can join against observation data. #### Can I get the order book and trade history? Yes — `includeOrderbook` attaches the book as `{"yes_dollars": [[price, size], ...], "no_dollars": [[price, size], ...]}` (lists of bid levels), `includeTrades` attaches up to 1,000 recent fills per market (price, size, taker side, timestamp). With either flag off, the corresponding key is omitted from the record entirely — use `.get()`. #### Is this affiliated with Kalshi? Does it trade? No and no. It reads Kalshi's public, unauthenticated market-data API. No account, no credentials, no orders — data only. ### Use with AI agents (MCP) Connect your agent to the Apify MCP server (mcp.apify.com) and it can call this actor as a tool: ask for "current KXHIGH brackets in Chicago" or "settled NYC daily-high markets from the last 30 days" and the agent fills the input schema (cities, seriesType, mode, lookbackDays) itself. A clean market-data source for trading agents and prediction-market research. ### More prediction-market data actors - [METAR & NWS Weather Station Observations API](https://apify.com/bigdavidson/metar-nws-weather-station-observations) - the raw station observations these markets settle on. - [Prediction Market Resolution Evidence Checker](https://apify.com/bigdavidson/prediction-market-resolution-evidence) - paste a market URL, get the live observation that resolves it, graded. # Actor input Schema ## `cities` (type: `array`): City names or aliases: NYC, Chicago, Miami, Austin, LA, Denver, Phoenix, Philly, Houston, Minneapolis, OKC, SF, DC, Boston, Dallas, Seattle, Las Vegas, Atlanta, San Antonio, NOLA — or "all" for every supported city. ## `seriesType` (type: `string`): KXHIGH = daily-high temperature markets, KXLOW = daily-low temperature markets, or both. ## `mode` (type: `string`): live = currently tradeable bracket markets with current bid/ask. settled = finalized markets (results + settled temperatures) within the lookback window. both = live + settled. ## `lookbackDays` (type: `integer`): How many days of settled-market history to pull (markets whose close time falls inside the window). Only used when mode is settled or both. ## `includeTrades` (type: `boolean`): Attach recent trades (up to 1,000 most recent fills) to each market record. Adds one API call per market. ## `includeOrderbook` (type: `boolean`): Attach the current order book (yes/no bid levels with fractional-contract sizes) to each market record. Adds one API call per market. Settled markets have empty books. ## Actor input object example ```json { "cities": [ "NYC" ], "seriesType": "both", "mode": "live", "lookbackDays": 90, "includeTrades": false, "includeOrderbook": false } ``` # API You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup. ## JavaScript example ```javascript import { ApifyClient } from 'apify-client'; // Initialize the ApifyClient with your Apify API token // Replace the '' with your token const client = new ApifyClient({ token: '', }); // Prepare Actor input const input = { "cities": [ "NYC" ], "seriesType": "both", "mode": "live" }; // Run the Actor and wait for it to finish const run = await client.actor("bigdavidson/kalshi-weather-markets").call(input); // Fetch and print Actor results from the run's dataset (if any) console.log('Results from dataset'); console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`); const { items } = await client.dataset(run.defaultDatasetId).listItems(); items.forEach((item) => { console.dir(item); }); // 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs ``` ## Python example ```python from apify_client import ApifyClient # Initialize the ApifyClient with your Apify API token # Replace '' with your token. client = ApifyClient("") # Prepare the Actor input run_input = { "cities": ["NYC"], "seriesType": "both", "mode": "live", } # Run the Actor and wait for it to finish run = client.actor("bigdavidson/kalshi-weather-markets").call(run_input=run_input) # Fetch and print Actor results from the run's dataset (if there are any) print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"]) for item in client.dataset(run["defaultDatasetId"]).iterate_items(): print(item) # 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start ``` ## CLI example ```bash echo '{ "cities": [ "NYC" ], "seriesType": "both", "mode": "live" }' | apify call bigdavidson/kalshi-weather-markets --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=bigdavidson/kalshi-weather-markets", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Kalshi Weather Markets Data — KXHIGH & KXLOW", "description": "Live bracket prices and settlement history for every Kalshi KXHIGH/KXLOW weather market: per-bracket bid/ask, open interest, settled temps by city and date. 20 cities mapped to their exact settlement stations. No login, no API keys. From $1 per 1,000 markets.", "version": "0.1", "x-build-id": "1kZ73aGLNIHDQ8cIb" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/bigdavidson~kalshi-weather-markets/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-bigdavidson-kalshi-weather-markets", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/bigdavidson~kalshi-weather-markets/runs": { "post": { "operationId": "runs-sync-bigdavidson-kalshi-weather-markets", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK", "content": { "application/json": { "schema": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/bigdavidson~kalshi-weather-markets/run-sync": { "post": { "operationId": "run-sync-bigdavidson-kalshi-weather-markets", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } } }, "components": { "schemas": { "inputSchema": { "type": "object", "properties": { "cities": { "title": "Cities", "type": "array", "description": "City names or aliases: NYC, Chicago, Miami, Austin, LA, Denver, Phoenix, Philly, Houston, Minneapolis, OKC, SF, DC, Boston, Dallas, Seattle, Las Vegas, Atlanta, San Antonio, NOLA — or \"all\" for every supported city.", "default": [ "NYC" ], "items": { "type": "string" } }, "seriesType": { "title": "Series type", "enum": [ "KXHIGH", "KXLOW", "both" ], "type": "string", "description": "KXHIGH = daily-high temperature markets, KXLOW = daily-low temperature markets, or both.", "default": "both" }, "mode": { "title": "Mode", "enum": [ "live", "settled", "both" ], "type": "string", "description": "live = currently tradeable bracket markets with current bid/ask. settled = finalized markets (results + settled temperatures) within the lookback window. both = live + settled.", "default": "live" }, "lookbackDays": { "title": "Lookback days (settled mode)", "minimum": 1, "maximum": 365, "type": "integer", "description": "How many days of settled-market history to pull (markets whose close time falls inside the window). Only used when mode is settled or both.", "default": 90 }, "includeTrades": { "title": "Include trade history", "type": "boolean", "description": "Attach recent trades (up to 1,000 most recent fills) to each market record. Adds one API call per market.", "default": false }, "includeOrderbook": { "title": "Include order book", "type": "boolean", "description": "Attach the current order book (yes/no bid levels with fractional-contract sizes) to each market record. Adds one API call per market. Settled markets have empty books.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } } } ```